Halloween candy

Task 4 - Halloween Candy Data

The data is in clean formt now we can use it to ananlyse

library(tidyverse)
library(dplyr)
library(stringr)
library(janitor)
library(here)

# Load data from first csv file 

halloween_candies <- 
  read_csv(here("clean_data/halloween_candies.csv"))

1.4.2 Analysis questions

Q1

# What is the total number of candy ratings given across the three years

halloween_candies %>%
  group_by(ratings)%>%
  drop_na(ratings)%>%
  summarise(candy_ratings = n()) %>%
  summarise(total_ratings = sum(candy_ratings))

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Q2

# What was the average age of people who are going out trick or treating and the average age of people 3. not going trick or treating?

halloween_candies %>%
   group_by(going_out) %>%
   summarise(avg_age = mean(age, na.rm = TRUE))

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Q3

# For each of joy, despair and meh, which candy bar revived the most of these ratings?

  
halloween_candies %>%
  drop_na(ratings) %>%
  group_by(candies, ratings) %>%
  summarise(candy_ratings = n()) %>%
  filter(candy_ratings == max(candy_ratings))

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Q4

# How many people rated Starburst as despair

halloween_candies %>%
  filter(candies == 'starburst') %>%
  group_by(candies, ratings) %>%
  drop_na(ratings) %>%
  summarise(starburst_ratings = n()) %>%
  filter(ratings == 'despair')

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Q5

# What was the most popular candy bar by this rating system for each gender in the dataset?

halloween_candies %>%
  filter(gender == c('male', 'female')) %>%
  group_by(candies, gender) %>%
  drop_na(ratings) %>%
  summarise(popular_ratings = sum())

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